Renaming in Adoption: Exploring Name Ambivalence in Adoptive Parents' Name Stories
Bibliographic record
Abstract
ABSTRACT Names are central to identity, yet their role in adoption, where identity and family dynamics are complex, remains under‐researched. This article draws on findings from a qualitative study of names and adoption in England and Wales to examine adoptive parents' decisions about the first names of their children. Despite policy, which advocates the retention of a child's birth name, we found that half of the adopter participants' children had had their first names altered. Our data show that adopters' decisions about renaming their children were imbued with mixed emotions and reflected ‘name ambivalence’—the coexistence of conflicting feelings about birth names and a desire to change them. We examine this ambivalence through four elements: compromising, rationalising, minimising and acceptance. Through this analysis, we offer insight into the complex interplay between names, identity and adoptive family dynamics and consider implications for policy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".